Multi-Scale Cortical Dynamics and Seizure Prediction in Human Focal Epilepsy

描述（由申请人提供）： 照顾癫痫退伍军人是退伍军人事务部的优先事项，全国各地建立了几个癫痫卓越中心（ECoE）。癫痫发作的持续但似乎不可预测的复发极大地影响了患有癫痫的退伍军人和平民的自主权和生活质量。不幸的是，25-35%的癫痫患者尽管接受了最大限度的药物治疗，但仍继续癫痫发作（Annegers 2001）。区域性手术切除除了具有显著的风险外，仅在约40-70%的病例中是治愈性的（Engel等人，2003年）。即使药物或手术方法有效，副作用也可能很严重。替代的癫痫预防和预警系统将是非常可取的，以恢复退伍军人和平民的生活质量和自主权， 癫痫在过去的30年里，基于颅内EEG（iEEG）记录的癫痫发作预防和预警系统得到了广泛的研究，但可靠的癫痫发作预测和早期检测仍然是难以捉摸的。总的来说，由于难以监测人类单个神经元集合的活动，进展受到了阻碍。我们将使用一种新的技术，皮质内96微电极阵列（MEA），以更高的空间分辨率比iEEG记录检查人类癫痫。我们将在接受切除术前监测的难治性局灶性癫痫患者中记录单个神经元（单个单位，SU）和多通道多单位（MU）和高密度场电位（LFP）的活动。AIM 1将研究癫痫发作如何在多个尺度上开始，传播和终止，从SU，MU和LFP集合的微生理水平到iEEG信号中反映的宏观动力学。我们将测试的假设，神经元合奏动力学癫痫发作前逐渐变化，这些变化可以检测到癫痫发作前几分钟。我们进一步假设，这种转变应该体现在SU，MU和LFP之间的时空相关性的变化。AIM 2a将开发和测试一种新的框架，用于基于局灶性癫痫患者的多尺度MEA神经信号进行癫痫发作预测和早期预警。AIM 2b将比较癫痫发作预测的新框架与完全基于iEEG的最新预测方法。这种比较将揭示包括MEA神经信号的癫痫发作预测方法与不包括MEA神经信号的方法的优点和缺点。

DESCRIPTION (provided by applicant): The care of Veterans with epilepsy is a priority for the Department of Veterans Affairs as attested by the creation of several Epilepsy Centers of Excellence (ECoE) across the country. The constant but seemingly unpredictable re-occurrence of seizures dramatically impacts the autonomy and quality of life of Veterans and civilians suffering from epilepsy. Unfortunately, 25-35% of patients with epilepsy continue to have seizures despite maximal medical therapy (Annegers 2001). Regional surgical resection, besides carrying significant risk, is curative in only ~ 40-70% of cases (Engel et al., 2003). Even when pharmacological or surgical methods work, side effects can be severe. Alternative seizure prevention and warning systems would be highly desirable to restore quality of life and autonomy of Veterans and civilians with intractable epilepsy. Seizure prevention and warning systems based on intracranial EEG (iEEG) recordings have been intensively examined in the past 30 years, yet reliable seizure prediction and early detection remain elusive. Overall, progress has been hampered by the difficulty of monitoring the activity of ensembles of single neurons in humans. We will use a novel technology, intracortical 96-microelectrode arrays (MEAs), to examine human epilepsy at a much higher spatial resolution than in iEEG recordings. We will record the activity of ensembles of single neurons (single units, SUs), and multichannel multi-unit (MU) and high-density field potentials (LFPs) in patients with pharmacologically intractable focal epilepsy undergoing pre-resection surgery monitoring. AIM 1 will study how seizures start, spread and terminate at multiple scales, from the microphysiological level of ensembles of SUs, MUs and LFPs to the macroscopic dynamics reflected in iEEGs signals. We will test the hypothesis that neuronal ensemble dynamics preceding seizure onset change gradually and that these changes can be detected several minutes before the seizure onset. We further hypothesize that this transition should manifest in changes in spatiotemporal correlations among SUs, MUs and LFPs. AIM 2a will develop and test a new framework for seizure prediction and early warning based on multi-scale MEA neural signals in people with focal epilepsy. AIM 2b will compare this new framework for seizure prediction with state of the art prediction approaches based exclusively on iEEG. This comparison will reveal the advantages and disadvantages of seizure prediction approaches that include MEA neural signals versus approaches that do not.